Computer aided fuzzy medical diagnosis
ABSTRACT This paper describes a fuzzy approach to computer aided medical diagnosis in a clinical context. It introduces a formal view of diagnosis in clinical settings and shows the relevance and possible uses of fuzzy cognitive maps and fuzzy logic. A constraint satisfaction method is introduced which uses the temporal uncertainty in symptom durations that may occur with particular diseases. Together with fuzzy symptom descriptions, the method results in an estimate of the stage of a disease if the temporal constraints of the disease in relation to the occurrence of the symptoms are satisfied. The approach is evaluated through simulation experiments showing the effects of symptom ordering, temporal uncertainty and symptom strengths on the diagnosis efficiency. The method is effective and can be developed further using second order (Type 2) fuzzy logic to better represent uncertainty in the clinical context thus improving differential diagnosis accuracy.
Conference Proceeding: A Mathematical Description of Physician Decision Making[show abstract] [hide abstract]
ABSTRACT: This paper presents a mathematical model describing how the physicians actually make a diagnostic decision. Next to a description of diagnostic decision making as done by the physicians, this paper shows that how can we design and develop a new approach to medical diagnosis based on the human principles of decision making. The model was challenged to diagnose a series of actual patients. Real clinical data was entered into the model and the system produced a ranked list of possible diagnoses for each case. The results indicated good performance when compared with internist's diagnosis. The proposed method is effective and can be applied to describe medical reasoning as done by the physicians.Bioinformatics and Biomedical Engineering, 2008. ICBBE 2008. The 2nd International Conference on; 06/2008
Chapter: How do physicians make a decision?01/2007: pages 696-699; , ISBN: 978-3-540-73044-6
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ABSTRACT: Fuzzy Cognitive Maps (FCMs) are a convenient tool for modeling and simulating dynamic systems. FCMs were applied in a large number of diverse areas and have already gained momentum due to their simplicity and easiness of use. However, these models are usually generated manually, and thus they cannot be applied when dealing with large number of variables. In such cases, their development could be significantly affected by the limited knowledge and skills of the designer. In the past few years we have witnessed the development of several methods that support experts in establishing the FCMs or even replace humans by automating the construction of the maps from data. One of the problems of the existing automated methods is their limited scalability, which results in inability to handle large number of variables. The proposed method applies a divide and conquer strategy to speed up a recently proposed genetic optimization of FCMs. We empirically contrast several different designs, including parallelized genetic algorithms, FCM-specific designs based on sampling of the input data, and existing Hebbian-based methods. The proposed method, which utilizes genetic algorithm to learn and merge multiple FCM models that are computed from subsets of the original data, is shown to be faster than other genetic algorithm-based designs while resulting in the FCMs of comparable quality. We also show that the proposed method generates FCMs of higher quality than those obtained with the use of Hebbian-based methods.Fuzzy Sets and Systems. 01/2010;